Outputs (2285)

Into the Wild: Beyond the Design Research Lab (2020)
Book
Chamberlain, A., & Crabtree, A. (Eds.). (2020). Into the Wild: Beyond the Design Research Lab. Springer International Publishing. https://doi.org/10.1007/978-3-030-18020-1

This edited collection opens up new intellectual territories and articulates the ways in which academics are theorising and practicing new forms of research in ‘wild’ contexts. Many researchers are choosing to leave the familiarity of their laborator... Read More about Into the Wild: Beyond the Design Research Lab.

Neural Network based Weighting Factor Selection of MPC for Optimal Battery and Load Management in MEA (2020)
Presentation / Conference Contribution
Wang, X., Gao, Y., Atkin, J., & Bozhko, S. (2020, November). Neural Network based Weighting Factor Selection of MPC for Optimal Battery and Load Management in MEA. Presented at 2020 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan

This paper presents a Neural Network (NN)-based weighting factor (WF) selection method for the multi-objective cost function in Model Predictive Control (MPC). MPC is adopted for scheduling the loads and charging/discharging the battery intelligently... Read More about Neural Network based Weighting Factor Selection of MPC for Optimal Battery and Load Management in MEA.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, Article 107033. https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Measuring Mental Workload Variations in Office Work Tasks using fNIRS (2020)
Journal Article
Midha, S., Maior, H. A., Wilson, M. L., & Sharples, S. (2021). Measuring Mental Workload Variations in Office Work Tasks using fNIRS. International Journal of Human-Computer Studies, 147, Article 102580. https://doi.org/10.1016/j.ijhcs.2020.102580

The motivation behind using physiological measures to estimate cognitive activity is typically to build technology that can help people to understand themselves and their work, or indeed for systems to do so and adapt. While functional Near Infrared... Read More about Measuring Mental Workload Variations in Office Work Tasks using fNIRS.

Framing governance for a contested emerging technology:insights from AI policy (2020)
Journal Article
Ulnicane, I., Knight, W., Leach, T., Stahl, B. C., & Wanjiku, W.-G. (2021). Framing governance for a contested emerging technology:insights from AI policy. Policy and Society, 40(2), 158-177. https://doi.org/10.1080/14494035.2020.1855800

This paper examines how the governance in AI policy documents have been framed as way to resolve public controversies surrounding AI. It draws on the studies of governance of emerging technologies, the concept of policy framing, and analysis of 49 re... Read More about Framing governance for a contested emerging technology:insights from AI policy.

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design (2020)
Journal Article
Jiang, X., Bai, R., Wallace, S. W., Kendall, G., & Landa-Silva, D. (2021). Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design. Computers and Operations Research, 128, Article 105182. https://doi.org/10.1016/j.cor.2020.105182

© 2020 Elsevier Ltd We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scena... Read More about Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design.

Artificial intelligence for human flourishing – Beyond principles for machine learning (2020)
Journal Article
Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Laulhé Shaelou, S., Patel, A., Ryan, M., & Wright, D. (2021). Artificial intelligence for human flourishing – Beyond principles for machine learning. Journal of Business Research, 124, 374-388. https://doi.org/10.1016/j.jbusres.2020.11.030

The technical and economic benefits of artificial intelligence (AI) are counterbalanced by legal, social and ethical issues. It is challenging to conceptually capture and empirically measure both benefits and downsides. We therefore provide an accoun... Read More about Artificial intelligence for human flourishing – Beyond principles for machine learning.

Test Record (2020)
Presentation / Conference Contribution
Veasey, B., CREATORS_FN: Price, C. V., CREATORS_FN: Green, C. J., CREATORS_FN: Sperring, C. D., CREATORS_FN: Houghton, C. L., Bloggs, & Person, N. U. (2017, August). Test Record. Presented at CHI 2017: ACM CHI Conference on Human Factors in Computing Systems, Magicland

ABSTRACT: This is a test record for the purposes of analysis of collected meta against particular input fieldname titles and how it is exposed through JSON export from eprints (Everything Version)

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This is an adjusted abstract with two ne... Read More about Test Record.

Response to Consultation on the National Data Strategy (2020)
Other
McAuley, D., Edwards, L., & Chen, J. Response to Consultation on the National Data Strategy

Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance (2020)
Journal Article
Santiago Júnior, V. A. D., Özcan, E., & Carvalho, V. R. D. (2020). Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance. Applied Soft Computing, 97(Part A), Article 106760. https://doi.org/10.1016/j.asoc.2020.106760

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement Learn... Read More about Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance.